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EMG Sensors as Virtual Input Devices

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MIDI '14: Proceedings of the 2014 Mulitmedia, Interaction, Design and Innovation International Conference on Multimedia, Interaction, Design and Innovation

Pages 1 - 5

https://doi.org/10.1145/2643572.2643591

Published: 24 June 2014 Publication History

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Abstract

Electromyography (EMG), i.e. the detection and analysis of electric signals related to activity of muscles in humans, is often used to control prosthetic limbs. Recently, its importance started to grow also in the field of the human--computer interaction. We have established a simple electromyographic signal pipeline using two off-the-shelf hardware devices NIA in order to implement a virtual input device capable of emulation of mouse. A single NIA device is capable of signal composed of various components, including the EMG signal. We tested the reliability of recognition of elementary activities, such as fist clench, eye blink, etc. and the use of NIA as device which controls an emulation of the mouse. The placement on the forehead yielded least amount of errors.

References

[1]

Chu, J.-U., Moon I. and Mun. M. A real-time pattern recognition for multifunction myoelectric hand control. IEEE Transactions on Biomedical Engineering, 3 (2006), pp. 2232--2239,

Google Scholar

[2]

Eldin Henry Shroffe D. and Manimegalai, P. Hand gesture recognition based on emg signals using ann. International Journal of Computer Application, 2 (2013), pp. 31--39.

Google Scholar

[3]

Felzer, T. and Freisleben, B. Hawcos: the "hands-free" wheelchair control system. In Proc. Assets '02, ACM Press (2002), 127--134.

Digital Library

Google Scholar

[4]

Felzer, T., MacKenzie, I., Beckerle, P. and Rinderknecht, S. Qanti: A Software Tool for Quick Ambiguous Non-standard Text Input. In Computers Helping People with Special Needs. Miesenberger, K., Klaus, J. Zagler, W. and Karshmer, A. (Eds.), Springer Berlin Heidelberg, 2010, LNCS, Vol. 6180, 128--135.

Digital Library

Google Scholar

[5]

Felzer, T. and Nordmann. How to operate a pc without using the hands. In Proc. Assets '05. ACM Press (2005), 198--199.

Digital Library

Google Scholar

[6]

Felzer, T. and Nordmann, R. Alternative text entry using different input methods. In Proc. Assets '06, ACM Press (2006), 10--17.

Digital Library

Google Scholar

[7]

Kherzi, M. and Jahed, M. Real-time intelligent pattern recognition algorithm for surface emg. Biomedical Engineering OnLine, (2007), pp. 1--12.

Google Scholar

[8]

MacKenzie, S. and Felzer, T. Sak: Scanning ambiguous keyboard for efficient one-key text entry. In ACM Transactions on Computer-Human Interaction (TOCHI), Vol. 17, Issue 3, (July 2010), 11:1--11:39.

Digital Library

Google Scholar

[9]

Ward, D. J., Blackwell, A. F. and MacKay, D. J. C. Dasher -- a data entry interface using continuous gestures and language models. In Proc. UIST '00, ACM Press (2000), pp. 129--137.

Digital Library

Google Scholar

[10]

Wheeler, K. R., Chang, M. H. and Knuth, K. H. Gesture-based control and emg decomposition. IEEE Transactions on Systems Man and Cybernetics Part C-Applications and Reviews, 36 (4), (2006), pp. 503--514.

Digital Library

Google Scholar

Cited By

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Sehrt JWißmann TBreitenbach JSchwind V(2023)The Effects of Body Location and Biosignal Feedback Modality on Performance and Workload Using Electromyography in Virtual RealityProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580738(1-16)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3580738
Yang QZou YZhao MLin JWu KSchulzrinne HLi P(2018)ArmInProceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services10.1145/3286978.3287030(117-126)Online publication date: 5-Nov-2018
https://dl.acm.org/doi/10.1145/3286978.3287030

Index Terms

EMG Sensors as Virtual Input Devices
1. Human-centered computing
  1. Human computer interaction (HCI)

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Published In

MIDI '14: Proceedings of the 2014 Mulitmedia, Interaction, Design and Innovation International Conference on Multimedia, Interaction, Design and Innovation

June 2014

157 pages

ISBN:9781450327794

DOI:10.1145/2643572

General Chair:
Marcin Sikorski
PJWSTK, Warsaw (Poland) and Gdansk University of Technology, Gdansk (Poland)
,
Program Chair:
Krzysztof Marasek
PJWSTK, Warsaw (Poland)

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 June 2014

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MIDI '14

MIDI '14: Mulitmedia, Interaction, Design and Innovation International Conference

June 24 - 25, 2014

Warsaw, Poland

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MIDI '14 Paper Acceptance Rate 17 of 33 submissions, 52%;

Overall Acceptance Rate 35 of 62 submissions, 56%

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Cited By

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Sehrt JWißmann TBreitenbach JSchwind V(2023)The Effects of Body Location and Biosignal Feedback Modality on Performance and Workload Using Electromyography in Virtual RealityProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580738(1-16)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3580738
Yang QZou YZhao MLin JWu KSchulzrinne HLi P(2018)ArmInProceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services10.1145/3286978.3287030(117-126)Online publication date: 5-Nov-2018
https://dl.acm.org/doi/10.1145/3286978.3287030

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Recommendations

Predicting EMG Based Elbow Joint Torque Model Using Multiple Input ANN Neurons for Arm Rehabilitation

Human motion intention recognition based on EMG signal and angle signal

Detection of human reflex response in EMG signals: a time-frequency approach